[1]} and [2]} proposed a graph convolutional network (GCN) model which used simple linear transformations to update the node states, unlike the graph LSTMs used by [3]} and [4]}. [1]} gave equal weights to the edges, whereas [2]} used an attention mechanism to assign different weights to the edges. [7]}, [8]}, and [9]} used graph convolutional networks for sentence-level relation extraction. They considered each token in a sentence as a node in the graph and used the syntactic dependency tree to create a graph structure among the nodes. [7]} used the GCN in a multi-instance setting. They used a Bi-GRU layer and a GCN layer over the full dependency tree of the sentences to encode them. The sentence representations in a bag were aggregated and passed to a classifier to find the relation. Following [11]}, [8]} used only the shortest dependency path (SDP) tree to build the adjacency matrix for the graph. Along with the SDP tree, they included the edges that are distance \(K\)  away from the SDP where \(K\)  is a hyper-parameter. [9]} proposed a soft pruning strategy over the hard pruning strategy of [8]} in their GCN model. They considered the full dependency tree to build the adjacency matrix but using a multi-head self attention-based soft pruning strategy, they can identify the important and unimportant edges in the graph.
